Method for aerial imagery acquisition and analysis

ABSTRACT

A method and system for multi-spectral imagery acquisition and analysis, the method including capturing preliminary multi-spectral aerial images according to pre-defined survey parameters at a pre-selected resolution, automatically performing preliminary analysis on site or location in the field using large scale blob partitioning of the captured images in real or near real time, detecting irregularities within the pre-defined survey parameters and providing an output corresponding thereto, and determining, from the preliminary analysis output, whether to perform a second stage of image acquisition and analysis at a higher resolution than the pre-selected resolution. The invention also includes a method for analysis and object identification including analyzing high resolution multi-spectral images according to pre-defined object parameters, when parameters within the pre-defined object parameters are found, performing blob partitioning on the images containing such parameters to identify blobs, and comparing objects confined to those blobs to pre-defined reference parameters to identify objects having the pre-defined object parameters.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/252,513 filed on Nov. 8, 2015.

FIELD OF THE INVENTION

The present invention relates to image acquisition and analysis, ingeneral and, in particular, to aerial imagery acquisition and analysis.

BACKGROUND OF THE INVENTION

Aerial remote sensing has been developing rapidly over the last decades.Remote sensing is being used for various purposes, among which areagricultural and other environmental monitoring purposes. With the rapiddevelopment of light aerial vehicles, such as drones, hovercraft andmany other types of UAVs, in addition to the affordable cost ofultra-light and light manned aerial vehicles, remote sensing, includingimage acquisition, is developing and accessible even to small scaleorganizations and farmers.

Aerial vehicles can be controlled and managed remotely. When flying overa city or a field, images can be captured for remote sensing purposes.The flight mission of unmanned aerial vehicles, just like manned onesequipped with an automatic pilot, can be pre-planned for specific routesaccording to the mission purposes, and also altered in real time, ifrequired.

NDVI (Normalized Difference of Vegetation Index) software and tools,which are the main vehicle for agricultural remote sensing measurement,like many remote sensing tools for other purposes, assist organizationsand farmers in monitoring the environment and fields/crops status. TheNDVI vegetation index (Normalized Difference Vegetation Index) measurecan be used to analyze remote sensing measurements, typically but notnecessarily from a space platform or an aerial vehicle, and assesswhether the target being observed contains live green vegetation or not.NDVI and other multi-spectral and hyper-spectral analysis tools, whenused properly and based on the right image acquisition equipment, canindicate, for example, the presence of jellyfish swarms and sardineflocks in the sea, military mines in shallow ground, the presence ofmetal objects on the ground, the stress and vigor of vegetation, dry/wetareas, forestry health and diseases, the presence of pests andlivestock, and so on. The resulting output of NDVI analysis and similartools can be presented as a simple graphical indicator (for example, abitmap image).

However, the analysis of NDVI and all other tools is usually doneoff-line (following the drone/airplane/satellite's acquisition) and theresulting image or set of images (orthophotos), which present differentareas of the image in various colors, are presented to the farmer/userafter a significant delay. In addition, for an average person/farmer,the output images of these tools are not of much value, as he is usuallynot an expert, and is unable to perform the necessary analyses in orderto fully understand what is presented in the bitmap. Furthermore, inmost cases based on the analyses results, the user is sent to the fieldfor a closer look, in order to find the exact nature of theirregularities which were indicated. There are services which analyzethe images and send a report to the farmer, but usually these reportsare prepared by a human expert, who examines the images in a similar waythat a physician examines an X-Ray radiograph, and such analyses aresent a day or a few days after the acquisition of the imagery, and asstated above, require in many cases additional and more detailedexploration.

A typical agricultural use of remote sensing can serve as a good exampleof such a need. If a farmer wants to have a survey in order to detect,in a timely manner, the presence of white fly or aphids in his crops,and the size of both white fly and aphids could be of 1 mm-2 mm only, itis clear that one cannot screen every inch of the crops searching forthem. However, there could be changes, which are evident in lowerresolution imagery (visual, hyper-spectral or multi-spectral images),which indicate that a certain area of a field could be infected withsome unidentified pests or diseases. Unfortunately, the best satelliteimagery (like GeoEye-1) is of 1 pixel per 40 cm, which is far fromsufficient for early detection of such pests. Aerial drone imaging,shooting, for example, with a 25 mm lens three meters above the groundcan cover a rectangular area of 1.6×2.4 m (3.84 square meters). Using a10 Mega Pixels camera, this means 26 pixels per square cm. Such detailedimagery could allow the identification of whitefly, but acquiring imagesof hundreds of acres at such resolution will require excessiveresources, and will turn the process into an impractical one.

Accordingly, there is a need for an intelligent method for automaticanalysis and a decision support system, which will allow not only forbetter remote sensing, but will provide the end user(organizations/farmers) with immediate or short term analysis resultsand advice, as in many cases delayed advice could be useless, and thedamage of a late analysis could be irreversible.

There are known image processing computer programs for analyzingacquired images of an object of unknown shape and comparing them toshapes in a database in order to identify the object. The imageclassification method based on a deep neural network approach is one ofthe most commonly used methods for this purpose.

In computer image analysis, blob detection methods are known fordetecting regions in a digital image that differ in properties, such asbrightness or color, compared to surrounding regions. Informally, a blobis a region of an image in which some properties are constant orapproximately constant; all the points in a blob can be considered insome sense to be similar to each other. In the blob partitioningmethodology, each digital image is comprised of grey level brightness,that is to say 256 levels of brightness. Each pixel in the image isassociated with one of these levels. The blob partitioning approachgroups adjacent pixels of the same brightness and represents them on adisplay as a discrete object or blob. That is to say, that the size ofeach blob is defined by the number of included pixels.

SUMMARY OF THE INVENTION

There is provided according to the present invention a method forautomatic analysis and a decision support system permitting acquisitionand analysis of multi-spectral image data, preferably in real or nearreal time. In particular, the method includes an initial imageacquisition and blob partitioning on a large scale, amounting tohundreds up to thousands of pixels per blob, for an initial analysis, inorder to determine whether to proceed with a second image acquisitionand blob partitioning on a small scale, grouping tens of pixels perblob, according to selected criteria in order to investigateirregularities in the initial images.

There is provided, according to the invention, a method formulti-spectral imagery acquisition and analysis, the method includingcapturing preliminary multi-spectral aerial images according topre-defined survey parameters at a pre-selected resolution,automatically performing preliminary analysis on site or location in thefield using large scale blob partitioning of the captured images in realor near real time, detecting irregularities within the pre-definedsurvey parameters and providing an output corresponding thereto, anddetermining, from the preliminary analysis output, whether to perform asecond stage of image acquisition and analysis at a higher resolutionthan the pre-selected resolution.

According to embodiments of the invention, the method further includesassociating GPS data with the detected irregularities, directing animage acquisition device, in real time or near real time, to captureadditional images of at least one of the detected irregularities athigher resolution than the pre-selected resolution using the associatedGPS data, and performing analysis using small scale blob partitioning ofthe captured images in real or near real time.

There is further provided, according to the invention, a system formulti-spectral imagery acquisition and analysis, the system including atleast one multi-spectral image capturing device, a processor coupled tothe image capturing device, the processor running an image processingmodule including a blob partitioning module to automatically analyzecaptured images by blob partitioning according to predefined surveyparameters and provide output corresponding to irregularities on eachimage falling within said predefined survey parameters, wherein the blobpartitioning module is capable of implementing both large scale blobpartitioning and small scale blob partitioning, and a geographicallocation indicator adapted and configured to provide an indication of ageographical location of the irregularities, the processor beingconfigured to automatically determine whether to direct one of themulti-spectral image capturing devices to the indicated geographicallocation to capture images of the irregularities in response to theoutput.

There is also provided, according to the invention, a method foranalysis and object identification including analyzing high resolutionmulti-spectral images according to pre-defined object parameters, whenparameters within the pre-defined object parameters are found,performing blob partitioning on the images containing such parameters toidentify blobs, and comparing objects confined to those blobs topre-defined reference parameters to identify objects having thepre-defined object parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be further understood and appreciated fromthe following detailed description taken in conjunction with thedrawings in which:

FIG. 1a shows a pair of images shot from a UAV flying above a plantationfield using suitable filters to obtain Visible and NIR images of thefield in accordance with one embodiment of the present invention;

FIG. 1b shows the pixels in FIG. 1a associated with selected NDVI valuessuperimposed on the visible image;

FIG. 1c shows the set of largest 34 blobs from FIG. 1b derived from thisset of pixels;

FIG. 2a shows a pair of Visible and NIR images of a given plantationfield;

FIG. 2b shows pixels associated with selected NDVI values rangingderived from the images of FIG. 2 a;

FIG. 2c shows a set of points associated with polygons having thehighest density values from FIG. 2 b;

FIGS. 3a and 3b illustrate a method where a detection is declared,according to one embodiment of the invention;

FIGS. 4a and 4b show a set of detected SMS pests resulting fromextracting small blobs whose colors are confined to a distance “close”to the white color;

FIGS. 5a and 5b illustrate a high correlation, flagging the presence SMSpests;

FIGS. 6a, 6b and 6c illustrate flagging of pest detection when thecorrelation measure exceeds a pre designed threshold value;

FIGS. 7a, 7b and 7c illustrate an original CMS pest, a binary imageresulting from the projection of the pixels of the largest three blobsof the original pest, and resulting boundaries of the pest afterprocessing, respectively;

FIGS. 8a and 8b illustrate a blob partition CMS-based detection method;

FIG. 9a shows a typical mine of a Citrus Leafminer;

FIGS. 9b and 9c shows boundaries and calculated corner points of theimage of FIG. 9 a;

FIG. 10 illustrates use of color manipulation to strengthen the colorsin the plant that are associated with blight; and

FIG. 11 is a block diagram illustration of a system constructed andoperative in accordance with embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides an inventive method for image acquisitionand analysis and a decision support system, which will provide betterremote sensing, and provide the end user (organizations/farmers) withenhanced imagery acquisition, analysis results and advice. Inparticular, the method permits a one- or two-stage acquisition andanalysis of multi-spectral imagery, wherein at least the first stageanalysis, at low resolution over a large area, is performed in real ornear real time. From results of this analysis, a determination is madewhether or not to proceed with a second stage of multi-spectral imageacquisition and analysis, at high resolution on a smaller area. Analysiscan be performed on location, i.e., at the time of acquisition of eachimage or frame with later addition of geographical indications, oranalysis can be performed on a geotagged orthophoto of a larger area.For purposes of the present invention, analysis in near real time, foreach acquired image, particularly on location (in the field) analysis,means no more than a few seconds (5 seconds or less) after theacquisition session, and, for creation and analysis of a geotaggedorthophoto covering a field of a few tens of hectares or more, near realtime means a few minutes (15 minutes or less) following acquisition.Providing a processor on board the aircraft that is capturing themulti-spectral images can allow analysis of the individual capturedframes and identification of areas of interest in real time. It is alsopossible, according to the current invention, to perform such near realtime or real time analysis if the captured imagery is transmitted over awireless network to a processing platform (e.g., computer) on theground.

According to the invention, analysis of the low resolution images isperformed using blob partitioning on a large scale, in order to identifyirregularities or regions of interest. The second stage of analysisutilizes blob partitioning on a small scale, when analyzing results ofhigh resolution image acquisition, for example, for pest detection andidentification. In particular, the invention utilizes acquisition andanalysis of multi-spectral data and not just of images in the visiblerange. According to some embodiments of the invention, the determinationas to whether to perform a second stage of acquisition and analysis ismade automatically.

Such an automatic decision support system will analyze the acquiredimagery, assisting the management of the acquisition process, and allowacquisition of more detailed imagery of selected geographical areas,based on the initial analysis of the preliminary captured images, thusenabling the examination of points of interest in the surveyed areas ata resolution which is impractical to acquire over a larger area. Amongother tasks, an enhanced imagery acquisition is concerned withaccurately delineating areas with specific required properties, definedin advance by the person requesting the survey, such as a specificdesired range of NDVI values or other measures.

The present invention provides an efficient solution for automaticallydelineating areas associated with desired NDVI—or other vegetation indexbased—values or any other selected parameters, by providing a method foradditional efficient visual and hyper-spectral or multi-spectralautomatic aerial imagery acquisition and analysis. This is accomplishedby acquiring the images at a resolution termed “low resolutionacquisition”, sufficient, during analysis, to allow accurate detectionof blobs whose associated values are confined to the required NDVI orother parameter range. This is performed, typically, by automaticallysearching for blobs indicating vegetation in a state of stress of somekind (for example, as manifested in NDVI values which are on averagelower by 15% to 20% than the optimal NDVI value). The stress couldindicate various issues regarding vegetation state and health, such asdryness, vigor, pests and diseases.

It will be appreciated that the image acquisition device can be arrangedto acquire multi-spectral images across a wide band of the spectrum.Alternatively, or in addition, the image acquisition device can bearranged to acquire images in a plurality of pre-selected bands or colorchannels, selected according to the objects being sought in the survey.In either case, analysis of the multi-spectral image will be performedutilizing acquired color channels selected according to the objectsbeing sought in the survey.

Preferably, the low resolution acquisition device uses a low distortioncamera, preferably using a NADIR gimbal. This enables securing avertical view, thus minimizing the distortion due to the camera angle.Alternatively, other suitable low resolution acquisition devices can beutilized. Preferably, the image acquisition device is equipped with anautomatic digital calibration apparatus, thus enabling the processing ofindividually captured images, without the need for complex alignment ofthe color channels, and avoiding the need for pre-processing, such asmorphing the images and distortion correction. Examples of suitableimage capturing apparatus are described in Applicants' pending U.S.patent application Ser. No. 62/260,272 filed 26 Nov. 2015.

The current invention allows fast and even real time analysis of theacquired imagery by its unique imagery acquisition and file type. Themulti-spectral image acquisition should preferably be carried out usinga file type (like JPEG, RAW, TIFF or any other image file format) whilethe multi-spectral channels (Blue, Green, Red, Red-Edge, NIR—all or someof them) are saved as multiple channels images side by side. This can beperformed most efficiently through the use of multiple lenses (asdescribed in the image capturing patent application cited above) using asingle sensor divided into separate areas, although the invention is notlimited to these examples. Using such a camera (having multiple lensesand a single sensor), the different RGB (Red Green Blue) and NIR (NearInfraRed) channels can be saved separately. Thus, for example, the redchannel can be captured on the left side of the sensor, while the nearinfra-red channel can be captured on the right side of the sensor. Also,different ranges of green and blue can be captured on the differentareas of the sensor, while all the channels are then saved in a singlefile. Such a capturing process allows simple splitting of up to 6distinctive channels using two lenses with a single sensor. Any side maycontain optimally up to three channels of shades of red, green and blue.Such a file structure allows simple and fast channel separation. As thedifferent red types (650 nm or 710 nm) are saved side by side with the850 nm channel, and the same way one can have different narrow blue andgreen channels, splitting the channels to RGB and separating the imageinto two (in case two lenses are used), will produce all the differentchannels. Such file format can be any standard RGB format file like JPG,BMP, PNG, TIFF etc., whereas these files are limited to 3 bands or 3bands and a Gamma channel. As important vegetation indices currently inuse employ more than two bands to accurately detect blobs of interest(such as ARVI—“Atmospherically Resistance Vegetation Index” whichemploys Red Blue and NIR bands), such side by side separation is vitalfor correct identification of blobs of interest.

The present invention utilizes blob detection methods to detect regionsin the digital images that differ in properties, such as brightness orcolor, compared to surrounding regions. Such regions or irregularitiesindicate possible problem areas that require further investigation. Thedetection of blobs associated with predefined parameters in the surveyedarea guides the second step of the imagery acquisition mission, the highresolution step. Such guidance can be accomplished by associating GPSdata with these blobs and directing an image capturing device on aUAV/drone/hovercraft/ultralight, etc., to take additional highlydetailed (higher resolution) images of points of interest(irregularities), whether in near-real time, real-time or postprocessing. Such detailed imagery acquisition can be done by the sameaerial vehicle and capturing device, or by the same vehicle usinganother capturing device, or by another aerial vehicle with a higherresolution capturing device, or by a ground vehicle with a capturingdevice.

Once highly detailed imagery is acquired (VIS, hyper-spectral ormulti-spectral imagery), it is possible to analyze the acquired imageryand assist the farmer in deciding what steps he needs to take,immediately or in the short term, or in the long term. This second stageof analysis is also performed using blob partitioning, typically smallscale blob partitioning.

In other words, the current invention applies automatic analysis offirst low and then high resolution imagery, in order to produce a fullyautomatic decision support system utilizing remote sensing devices. Lowresolution imagery can be the result of high altitude and/or a lowresolution camera and/or short focal length aerial imagery acquisition.Since lower resolution provides less data to process, low resolutionscanning is much faster and more efficient. High resolution detailedimagery can be acquired through low altitude acquisition and/or a highresolution camera and/or a long focal length (zoom) or even acquiringthe imagery from the ground.

According to the present invention, different methods are used for eachstep of the image acquisition process, in order to utilize efficientlythe image capturing devices, and perform fast and practical scanning ofthe environment/fields where possible in real time or near real time,while “focusing” in high resolution imagery on blobs of interest andidentifying, as accurately as possible, their nature according to therequired survey. For agricultural purposes, for example, the currentinvention implements automatic systematics to identify pests, diseases,and vegetation vigor and other aspects of the crops.

It is important to clarify that blobs of interest are defined inadvance, according to the purpose of the particular survey. If, forexample, landmines are to be found, then the pixels representing theirgraphic representation after the suitable analysis (using NDVI or anyother vegetation index based metric or pre-selected parameters) will bedefined as the interesting pixels. If, for example, cotton canopy is tobe found, then the pixels representing the white cotton bolls will bedefined as the interesting pixels and blobs.

The first step of one possible embodiment according to the currentinvention includes aerial acquisition of multi-spectral imagery ofrather low resolution. Such low resolution could be of 1 pixel per 100square centimeters (10 cm×10 cm) or even 1 pixel per square meter, orany other resolution similar to that captured by satellites, which hasbeen proven over the years to be adequate for remote sensing for therequired survey. In other words, if, for example, an agricultural surveyis focused on cotton canopy, it is already known (as documented invarious US DOA publications) that image acquisition can be done from analtitude of a few hundred meters using a 50 mm focal length camera. Insuch a case, it is known that a multi-spectral camera of four spectralbands (Red; Green; Blue and Near Infra-Red) can acquire the requiredimagery for NDVI presentation.

The second step of this agricultural remote-sensing example, accordingto the current invention, includes an analysis, according to NDVI(and/or one of the tools based on it). The resulting values of theanalysis can be presented as an image, which presents the areas coveredwith cotton in one color and those uncovered by cotton in another color.The same principle applies to most agricultural surveys, where dry orunhealthy vegetation or weeds will be “painted” one color and healthyvegetation will appear “painted” another color. These colored pixels aremerely representing the results of the analyses in a user-friendly way,and the automatic analysis uses the values, which are attached to thesepixels.

The third step, according to the current invention, includes theautomatic gathering of the pixels associated with the pre-definedparameters, e.g., NDVI values (or any other agricultural vegetationindex metric) during image analysis, into blobs. The creation of suchblobs entails a uniform quantization operation that is dependent on therequired survey. In some surveys (like whitefly detection), very fewpixels cannot be ignored and even small blobs are of significance, whilein other surveys (like tuna school detection) only large blobs are ofimportance. As the NDVI is actually a synthetic image, i.e., each pixelhas a calculated value taken from RED and NIR channels, prior to anysuch blob creation, the NDVI map may undergo an advanced de-noisingoperation allowing the creation of continuous blobs that optimallypreserves important features of the acquired image.

This preliminary analysis is used by the system processor to determinewhether a second stage of image acquisition and analysis is required,for example, if irregularities are observed and if their extent or sizepasses a pre-selected threshold. If not, the survey ends and thisinformation is sent to the person requesting the survey. On the otherhand, if it is determined that a second stage is warranted, theprocessor either automatically proceeds, as describe hereafter, ornotifies the person requesting the survey and awaits manualinstructions. Alternatively, the analysis of the images after blobpartitioning can be accomplished manually (visually by a human being) inorder to determine whether to proceed to a second step of acquisition.

In the case of automatic acquisition and analysis, the fourth stepincludes the attachment of GPS data to the blobs indicatingirregularities on which the survey is focused (pest infested fields,water leaks, etc.). The GPS data can be attached to the images invarious ways. Some cameras have an embedded GPS sensor integrated withthem. In other cases, cameras are operated wirelessly by an operatingsmartphone (as is the case with Sony's QX1 lens-like camera and OlympusAir A01 camera, which can add the GPS data from the operating smartphoneor an autopilot/processing platform attached to the camera). Thepreferred solution, according to the current invention, due to betteraccuracy, is to add the GPS data from the aerial vehicle's GPS sensor,which is usually positioned on top of the vehicle and connected to theautopilot, or directly to the camera (if supported by the camera).Synchronizing the capturing time of the imagery and the GPS data is ofimportance, as aerial vehicles can move fast. It will be appreciatedthat each frame can have GPS data associated with it, or an orthophotoof a larger area can be geotagged.

Another way, and the most accurate one, is by creating an orthophoto ofthe acquired images and fitting it to an accurate map. Such matching,although it is slow and demands rather heavy computation, overcomes biasand deviation due to lens distortion, lack of satellite signals, and theangle of the vehicle while acquiring the imagery.

The fifth step, according to the current invention, includes theautomatic preparation of highly detailed imagery acquisition of theareas represented by the blobs of interest. The imagery acquisitiondevice (e.g., airborne or ground borne camera), which is preferably amulti-spectral or hyperspectral camera, is sent to the geographicallocation of these blobs to capture detailed imagery of those locations.Some possible ways to acquire highly detailed imagery are to use a lowaltitude hover craft or helicopter flying a few feet/meters above thecrop or sending a ground robot and/or a human being to acquire theimagery from the closest possible distance. Another method is to use acamera with a longer focal length of zoom. However, this method is quiteexpensive and inefficient, if hyper-spectral or multi-spectral highlydetailed imagery is required. The accuracy of the GPS data is ofimportance, as such close looks at the blobs of interest could mean, forexample, the acquisition of imagery of a 0.8 m×1.2 m rectangle at 10Mega Pixels, as described above.

It should be noted that in some surveys (like tuna school detection),the GPS data of a blob in a single image may not be sufficient, and atrajectory needs to be predicted based on a number of successive images,with an additional analysis of the GPS data changes of the blobs. Suchanalysis, calculated with the processing and acquisition time, willallow the system to follow the required blobs of interest, which are notstatic, and acquire detailed imagery of such blobs at their predictedlocation.

The sixth step, according to the current invention, includes automaticsystematics and identification of the findings. Systematics, orsystematic biology, for purposes of the invention, includes, inter alia,describing and providing classifications for the organisms, keys fortheir identification, and data on their distributions. This step is arather complex step, and it includes a few sub-steps in itself. It willbe appreciated that the automatic systematics and identification ofobjects described below can be utilized to analyze high resolutionimagery captured in any fashion, and not only by means of the automaticimage acquisition methods and system described above.

First, the acquired imagery is analyzed according to the survey type.For example, identifying greenfly on green leaves is more difficult thanidentifying whitefly, if the acquired imagery is of the visual spectrumonly. However, adding NIR imagery of greenfly on green leaves removesthe aphid's green camouflage, as the greenfly does not reflect the nearinfrared spectrum in the same way that chlorophyll does.

Second, once the presence of suspected objects (aphids, greenfly, worms,grasshoppers, etc.) is found, fragments of the imagery containing suchobjects are extracted, typically by drawing rectangular squares centeredat a centroid of each fragment, the squares being a few pixels long.Preferably, each fragment contains a single object with minimalbackground “noise”, to allow for better classification andidentification. The suspected objects are preferably first classified byutilizing a remote server containing a reference pests data baseaccording to the type of survey. A preferable way to classify thepests/diseases of an agricultural survey, for example, will include thelocation, crop type, and a reference to a bank of potentialpests/diseases, which are relevant to that specific type of crop.However, it is also possible to perform the automatic systematics in theacquisition platform. In this case, the acquisition platform will beequipped with an internal program capable of identifying the pest bycomparing it to a small data set of reference pest images residing inthe platform processing memory. The seventh step includes theidentification of the suspected (and classified, if they were classifiedin the previous step) objects.

The basic underlying idea for identifying the objects in this step isassociated with the claim that proper partitioning of the acquired imageinto confined size sub-images guarantees, in high probability, that, ifthe suspected objects are present in the detailed acquired image, theyare likely to be found in the confined size sub-images.

The size of the confined size sub-images is set according to the surveytype, matching the image size to that of the blobs which are supposed torepresent the objects which the survey is focused on. Once a set of suchconfined size sub-images, which contain blobs, is prepared, the processof identifying the objects within this set of sub-images can beaccomplished by either sending the set of these sub-images to a remoteserver for identification or by identifying the object on the spot byactivating a local identification code.

Various image partitioning methods and corresponding detection methodsare detailed in this invention. Generally, two types of objects aresearched for, objects having simple structure and objects having complexstructure. A few specific image partition-based methods are tailored foreach type.

The eighth step, according to the current invention, includes sendingthe results of the analysis to the addressee (the one who ordered thesurvey or the service provider). The results include the possibleidentification of the findings and they may also include adviceregarding the steps that need to be taken in order to, for example,exterminate a pest or put out a fire or to put a fence and warning signsaround old land mines (depending on the survey). The results can be sentwith the small fragments of the imagery from the cloud (Internet), oreven from the aerial vehicle, for example when the processing platformis a smartphone equipped with a cellular connection (e.g., 3G or LTE).In some possible embodiments of the current invention, the fractions ofthe imagery, the location and the advice to the user are sent throughmultimedia messaging service (MMS) or through an instant messagingservice (IM), like Messenger, WhatsApp, Skype, etc.

Detailed Description of Some of the Steps in an Exemplary AgriculturalSurvey

Identification of Areas Associated with Specific NDVI (and SimilarMetrics) Values

A preferred process will include the following steps:

-   -   1. Capturing visual and/or hyper-spectral or multi-spectral        imagery from an efficient range (preferably an altitude of a few        dozens of meters to a few hundred meters and even more);    -   2. Analyzing the imagery according to the desired survey (e.g.,        dryness; vegetation vigor; pests; diseases; etc.);    -   3. Defining the areas of interest in the images which call for        more detailed examination by large scale blob partitioning.    -   4. Attaching precise GPS data to the areas of interest.    -   5. Automatically directing the same aerial vehicle or another        vehicle with an image capturing device to these areas of        interest, directing it to take detailed (higher resolution)        imagery according to the survey type. Typically, the criteria        directing the platform to perform high resolution acquisition        will be the presence of blobs that are associated with NDVI        values 10% or 20% lower than the optimal NDVI value—i.e., an        indication of “vegetation stress”.

The overall goal here is to automatically detect, from an aerialvehicle, from relatively low resolution imagery, areas of interest thatare associated with specific NDVI (or some other tool) values. Suchareas of interest may typically indicate vegetation stress/vigor, wetareas, areas covered with water, ripe crop, vegetation vigor, etc. Asthe NDVI (or other vegetation index based) map is actually a syntheticimage, it entails an extremely large quantity of noise. Thus, apre-processing de-noising operation may be performed. In this invention,preferably an advanced de-noising algorithm, that preserves key featuresof the NDVI image, is employed. (See, for example, “Total VariationFilter”, Chambolle 2004). Alternatively, any other suitable de-noisingalgorithm can be utilized.

Automatic detection of such areas can be carried out by “gathering” intoblobs pixels which indicate deviation from the normal values for therequired survey (e.g., NDVI or EVI or any similar metric), preferablybased on a configurable number of pixels and density.

One possible embodiment of the process using the NDVI metric is asfollows:

The input for the calculations is a matrix of NDVI values ranging from−1 to 1, as acquired by a multi-spectral airborne camera. The NIR bandand the VIS_RED band are used to produce the NDVI values, using theformula NDVI=(NIR−VIS_RED)/(NIR+VIS_RED). Such NIR and VIS Red bandscould be narrow bands of 20 nm to 70 nm, the center of the bands being650 nm and 850 nm.

In one such possible embodiment of the invention, after having appliedan advanced de-noising operation on the NDVI map, blobs having a rangeof required NDVI values are searched. Pixels associated with requiredNDVI values are “gathered” to define blobs through the following threestep procedure:

Step 1. Assuming the area of interest is associated with a known rangeof NDVI values, or other predefined parameter values, the algorithmconstructs a binary map of the NDVI image, where the relevant pixelsassociated with the relevant range of NDVI values is set to 1 and therest of the pixels is set to 0.

Step 2. The set of all possible blobs (clusters) associated with thebinary map derived from step 1 is constructed using, for example, aCluster Labeling algorithm, or another suitable algorithm. Thisalgorithm is based on the “Hoshen Kopelman” cluster labeling algorithm(1975). Basically, the Hoshen Kopelman algorithm that is used in thisinvention raster scans a binary map and, in one pass, finds all theblobs along the scanning process and assigns them with a running index.

Other techniques for blobs construction, such as various methods forexhausted neighboring pixels search, can also be utilized, although theyincur computational load.

Step 3. A decision criterion to define a given group of blobs as anirregularity or an “area of interest, belonging to the required NDVIvalues” is defined as follows:

-   -   1. If the number of pixels belonging to a given single blob        exceeds a given threshold parameter.    -   2. The number of pixels belonging to a set of blobs, all        confined to a given radius (from the gravity center of the above        set of blobs), exceeds a given threshold.

FIGS. 1 a, 1 b and 1 c illustrate how the procedure works. FIG. 1a showsa pair of images shot from a UAV flying above a plantation field usingsuitable filters to obtain Visible and NIR images of the field. FIG. 1bshows the pixels associated with NDVI values ranging from −0.3 to −0.1(colored in red) (the pre-defined range of interest) superimposed on thevisible image. These pixels mark the wet areas in the field. FIG. 1cshows the set of 34 largest blobs (each blob, randomly colored,containing a number of pixels exceeding 200) derived from this set ofpixels that was calculated automatically using the algorithm describedabove.

In another embodiment of this invention, the detection of areasassociated with pre-selected NDVI values is carried out by the followingprocedure.

The binary map of pixels associated with NDVI required values ispartitioned into polygons having equal areas, and the density of eachpolygon is calculated. Polygons having large density are then chosen torepresent centers of areas associated with the required NDVI values.

FIGS. 2a, 2b and 2c show how this procedure works. FIG. 2a shows a pairof Visible and NIR images of a given plantation field. FIG. 2b showspixels associated with NDVI values ranging from 0.75 to 0.9 (thepre-defined range of interest) derived from the images of FIG. 2a . FIG.2c shows a set of points (marked in white color) associated withpolygons (actually blocks of size 4×4) having the highest densityvalues. The red points of the left image depict the set of all pixelswhose NDVI values range between 0.75 and 0.9.

The method described above can be implemented by a system 10 formulti-spectral imagery acquisition and analysis illustratedschematically in FIG. 11. The system 10 includes at least onemulti-spectral image capturing device 12 and a processor 14 coupled tothe image capturing device 12. Preferably, the processor is in two waycommunication with a user 20 for exchanging data and instructions. Theprocessor runs an image processing module 16 including a blobpartitioning module 18 to automatically analyze captured images by blobpartitioning according to predefined survey parameters and provideoutput corresponding to irregularities on each image falling within thepredefined survey parameters. As described above, this output can beused by the processor 14 or provided to the user 20. The blobpartitioning module 18 can implement both large scale blob partitioningand small scale blob partitioning. The system further includes ageographical location indicator 22 adapted and configured to provide anindication of a geographical location of the irregularities. Whilegeographical location indicator 22 is illustrated in this embodiment asbeing a separate unit, according to other embodiments of the invention,it can be part of the image capturing device 12. The processor isfurther configured to determine automatically whether to direct one ofthe multi-spectral image capturing devices to the indicated geographicallocation to capture images of the irregularities in response to theoutput.

The present invention also relates to a method for identifying objectsin multi-spectral images. The method includes analyzing high resolutionmulti-spectral images according to pre-defined object parameters and,when parameters within the pre-defined object parameters are found,performing blob partitioning on the images containing such parameters toidentify blobs. Objects confined to these blobs are compared topre-defined reference parameters to identify objects having thepre-defined object parameters. According to some embodiments, theobjects are classified before comparing. A number of non-limitingexamples of use of this method are as follows.

Automated Identification of Simple Morphology Objects

Some objects selected for identification possess the property of havingvery simple morphology (form and structure) (hereinafter “SMS”—SimpleMorphology Structure), typically in a shape of a small stain. One usefulapproach to detecting SMS objects entails calculation of the set of verysmall blobs (1 to 15 pixels), extraction of a small image around eachsmall blob, and searching for the selected object in this image.Typically, such search will involve activating an internal patternmatching program between the small blobs and reference patterns.

One important implementation of detecting SMS objects is its applicationto small pest detection. Pests' SMS images typically exhibit a shape ofa “small stain”, whose boundaries can be approximated by an ellipseshape.

Computerized identification of SMS pests in infected areas is based onthe following observational assumptions:

-   -   a. Images of SMS are geometrically significant, possessing an        ellipse-like shape and, usually, the color contrast of an SMS        pest with respect to its surroundings is very sharp.    -   b. The visual morphology of an infected plant containing a high        concentration of “stains”—SMS pests—in comparison to a        non-infected plant. Thus, an infected plant could be        distinguished from a non-infected plant by comparing the        “concentration of stains” in a suitable image processing tool.        The current invention employs methods for automatic detection of        SMS pests based on transforming the observational assumptions        described above into effective algorithms.

In one algorithmic embodiment of these observations, the acquired image(in Visible or NIR or NDVI image, or any kind of channel separation likeCMYK (cyan, magenta, yellow and black) is first converted to a graylevel image and then undergoes a gray level blob-based partitioning.Depending on the quality of the image, effective blob partitioningtypically will entail a uniform quantization operation. 32 values ofgray levels usually suffice for most applications. The binary map ofeach of the gray level values is subject to the Hoshen-Kopelman clusterlabeling algorithm described above, and finally the set of all possibleblobs consisting of all gray level values is created and stored.

From this set of calculated blobs, only small blobs having a size ofsmaller than a small threshold number of pixels (typically, but notlimited to, having a size smaller than 10 pixels) are extracted.

The image is partitioned to several, equal size sub-images. In eachsub-image, the number of small blobs created by the method describedabove is counted. A detection of the presence of SMS pests is declaredif the number of small blobs in a given percentage of the sub-imagesexceeds a pre-defined calibrated threshold value. Such threshold settingcan be set to match agricultural spraying policies.

In another algorithmic embodiment of this invention, the image isconverted to a gray level image and undergoes a gray level blobpartition using the same method described above. From the set ofpartitioned blobs, blobs having very small size, (typically but notlimited to having a size of less than 10 pixels) are selected and theirgravity centers are registered.

All sub images around each registered gravity center (having a radiuslength set to, typically, but not limited to, less than 50 pixels) areextracted and are compared to reference images of SMS pests, typicallyhaving an elliptical form, residing in the memory, using a correlationoperator or a pre-programmed deep neural network classifier function. Ifthe correlation exceeds a preset threshold value, and/or successfulclassification is flagged, a detection is declared. FIGS. 3a and 3bdemonstrate how this method works. The two gray level images depict aninfected cotton field. The set of red points superimposed on the imagein FIG. 3a shows the gravity centers of all small blobs whose number ofpixels is less than 50, as calculated by the method described above. Inthis example, the gray level image was reduced to 32 levels. Smallimages of 16×16 pixels size around each of these points were extractedand were subject to matching against reference images using acorrelation operator. The set of red points superimposed on the image inFIG. 3b shows all the sub images that the correlation operator found.

In another algorithmic embodiment of the analysis, the selection ofsmall blobs is confined to the set of blobs having a color that standswithin a close distance to a reference color that typicallycharacterizes the SMS pests in the query. In the current invention, thedistance between two given RGB colors is defined as follows:

If X1={R1,G1,B1} and X2={R2,G2,B2}

Then

Distance[X1,X2]=Max{Abs[R1−R2], Abs[G1−G2], Abs[B1−B2]};

FIGS. 4a and 4b shows the way this procedure works. FIG. 4a shows a leafcontaining a set of SMS pests (white color). FIG. 4b shows a set ofdetected SMS pests (indicated by purple points superimposed on the imageof FIG. 4a ) resulting from extracting small blobs whose colors areconfined to a distance “close” in the sense defined above, (in thisexample, less than 40 color units) to the white color. It will beappreciated that the color selected to indicate the pests in the displayis meaningless, in itself, and the display can be any selected color.

In another algorithmic embodiment of the analysis, the detection of SMSpests is carried out using the following method. An acquired gray-levelimage of the Visible image is partitioned into several, for example,four, very similar non-overlapping images, by forming a row wise andcolumn wise interlacing operator, resulting in forming four sub imageswhose union accurately spans the original image. Herein, it is assumedthat the original gray level image is marked as X, and X1, X2, X3 and X4are the resulting sub images due to the application of the interlacingoperator.

Defining the image A (“acceleration image of X”) as: A=(X1+X4−2*X3);will result in an image A that enhances contrasts. It is claimed thatextracting from A a set of a pre-selected number of pixels having thehighest brightness values yields a set of pixels that has a very highprobability to include SMS pests. Small sub images around the set ofcalculated high value pixels are extracted and their content is comparedto reference SMS pests' images residing in the memory. A highcorrelation will flag the presence SMS pests. FIGS. 5a and 5b illustratethis idea. FIG. 5a shows an infected leaf containing SMS pests and FIG.5b shows a set of red points, superimposed on FIG. 5a . The red pointsare the points created by the method described above, for which thecorrelator identified their corresponding extracted small sub images ascontaining an SMS pest.

In another embodiment of above algorithmic idea, detection of SMS pestsis carried out using the following method. The gray level of the Visibleor the NIR images undergoes an edge detection operator. From theresulting edge detected image, a set of pixels whose intensity valuesexceed a given threshold value is selected.

Small sub images around these pixels are extracted and their content arecompared against known reference images using a correlation method. Ifthe correlation measure exceeds a pre designed threshold value, pestdetection is flagged. FIGS. 6a, 6b and 6c demonstrate the idea. FIG. 6ashows a leaf containing SMS pests (colored in white), FIG. 6b shows anedge detection application of FIG. 6a and FIG. 6c shows a set of points(yellow) superimposed on FIG. 6a which is the result of extracting therelevant points from FIG. 6b in accordance with the method described.

In another embodiment of the above algorithmic idea, the detection ofSMS pests is carried out using the following method. The gray level ofthe Visible or the NIR images undergoes a binary threshold operation.The threshold level is calculated as follows: starting with a very highthreshold (dark frame), the threshold level is gradually decreased sothat, if the number of white points (resulting from the thresholdoperation) exceeds given a level, a desired threshold is declared.

From the resulting image, a set of pixels whose intensity values exceedthe above desired threshold value is selected. Small sub images aroundthese pixels are extracted and their content are compared against knownreference images using a correlation method. If the correlation measureexceeds a pre designed threshold value, pest detection is flagged.

Automated identification of Complex Morphology Objects

Some objects whose identification is required possess the property ofhaving a complex structure (here after CMS). A general approach fordetecting CMS objects entails partitioning the image into blobs havingmoderate size, depending on the specific required object morphology. Itis claimed that, if CMS objects are present in the acquired image, theyare very likely to be found in several images surrounding such moderatesized blobs, typically by transmitting these rather small images(usually confined only to few dozens of pixels) to a remote server foridentification, preferably, but not limited to, using a deep neuralnetwork approach. Another identification option is to activate aninternal code (such as described below) capable of identifying theobject.

A number of CMS pest identification methods will now be described.

Examples of CMS pests are fly, grasshopper, insects, etc. Observing thestructures of these pests shows very complicated morphology whosedetection usually calls for a sophisticated matching code.

In one embodiment of the pests CMS detection method, the acquired image(optionally VISIBLE and/or NIR and/or NDVI and/or other vegetation indexbased image) first undergoes gray level conversion and histogramequalization. The image then undergoes blob partitioning using theHoshen Kopelman cluster labeling approach described above.

Blobs having a moderate to high number of pixels are selected and subimages surrounding the gravity centers of these blobs having a radiusless than a given level (typically, but not limited to, 32 pixels) areextracted.

Each of the selected sub images undergoes further blob partition,preferably using 4 gray level values. Alternatively, a different numberof intensity values can be utilized. The calculated blobs are arrangedwith respect to their size in pixels. A selected number, for example,the first three large blobs, are extracted and a binary image comprisingtheir associated pixels is created. In case a pest is “captured” in theabove extracted sub image, and for the purpose of delineating themorphology boundaries, two morphology binary operators are applied onthe binary map comprising the large blobs: the binary dilate operatorand the binary erode operator. Finally, the difference between thedilated and the eroded images is calculated, resulting in an image thatapproximately captures the boundaries of the pest morphology.

FIGS. 7a, 7b and 7c demonstrate how the algorithm works. Each images inFIG. 7a shows an original CMS pest. Each image in FIG. 7b shows a binaryimage resulting from the projection of the pixels of the largest threeblobs of the corresponding image in FIG. 7a . And each image in FIG. 7cshows the resulting boundaries of the corresponding pest of FIG. 7aafter application of the binary erode and dilate operators. Theresulting images in FIG. 7c are then compared to similar type images,e.g., CMS pest boundaries, using a correlation procedure and/orclassification procedure using a deep neural network or other machinevision method, such as predictor and classifier.

In another embodiment of a blob partition CMS-based detection method,the gray level of the Visible image or the NIR image or the NDVI orother vegetation index based image undergoes blob partition (preferably,but not limited to, 4 intensity levels) and blobs having a selectedpixels size, for example, ranging from 50 to 300 pixels numbers, areextracted. The image surrounding each such blob is extracted and is sentto a remote server for classification to the right category of CMS pestsusing a deep neural network approach, or other machine vision method,such as predictor and classifier, compared against a large data set ofpest images that were used as a training set for pest classification.FIGS. 8a and 8b illustrate this concept. FIG. 8a depicts a leaf infectedwith CMS aphids, and FIG. 8b shows a set of blobs whose sizes rangebetween 100 and 200 pixels. The match between pests and extracted blobsis significant.

Automated Indirect Identification of Objects

In some cases, in particular in the case of automatic pestidentification, the presence of a pest can be detected by its uniqueimpact on its surroundings. In other words, it may be difficult orimpossible to capture the actual pest in the acquired images, but thepresence of the pest can be deduced from the state of the plant. Twosuch cases, out of many such situations, are presented below by way ofnon-limiting example only.

The pest known as “Citrus Leafminer” can serve as one good example. Thepresence of “winding” mines, attributed to this pest, are usually foundin the leaf surface. Thus, the detection of such mine manifests thepresence of this pest. The present invention presents two algorithmicmethods to identify this pest:

A. Using the method for automated identification of ComplexMorphological Structure, as described in the previous section, whereimages around blobs having a relatively large number of pixels(typically between 100 and 700) are extracted and the content of thisimage is compared to various reference images containing the CitrusLeafminer.

B. A specific Leafminer tailor-made detection algorithm is described asfollows: Images of blobs having a relatively large number of pixels areextracted. The boundaries of likely objects residing in these images arecalculated. On any such image, an Image Corner Filter is applied and thenumber of calculated corner coordinates is counted. If the number ofcorner coordinates exceeds a given threshold a detection of CitrusLeafminer is declared.

FIGS. 9a, 9b and 9c demonstrate the idea: FIG. 9a shows a typical mineof the Citrus Leafminer (encircled with yellow color) and FIG. 9b showsits boundaries and FIG. 9c shows the resultant corner points, resultingfrom application of the Corner Filter (bottom figure). Clearly there area relatively large number of such points, indicating the presence of amine.

It will be appreciated that the appearance of an object relative to itsbackground is different when viewed in different spectral bands.According to embodiments of the present invention, this fact can beutilized to improve the isolation and identification of various objectsof interest. For example, the high resolution multi-spectral image mayfirst undergo color manipulation to strengthen and isolate the soughtobject's appearance from the rest of the objects of the image. Thesemanipulations can include combining some of the original multi-spectralvisual bands and some channels from the CMYK (Cyan Magenta Yellow Key(Black)) Color space and/or HLS (Hue Lightness Saturation) Color spaceand/or any other color space or spaces. The usage of multi-spectral,narrow bands in this invention in various combinations betweenindividual bands from the RGB color space, CMYK color space, the HSLcolor space and possibly other color spaces, enables improved separationof the searched object from its surrounding background.

After the distinctiveness of the appearance of the disease or otherobject is examined in the various color channels and matrices, thearithmetical calculation which enhances this appearance is chosen. Suchcombinations of bands can be realized using various image processingtools, such as Image Difference, Add<Subtract, Lightest/Darkest pixels,image adjust, etc. The combination can be between two channels/matricesor more, according to the appearance of the object in the differentmatrices. This gives a much better object separation from the backgroundcolor than by using just the RGB color space. A further separation ofthe searched object, in general, can be achieved by also incorporatingthe specific 650 nm Red band of the multi-spectral spectrum. In thiscase, a pronounced white color indicates the detection of the object.Performing cross detection of various color combinations may improve thedetection. Various color combinations as described above, can bepre-selected and tailored to specific kinds of pests, thus increasingthe chances of the pest detection.

Another important example of automated indirect pest identification,according to these embodiments of the invention, is hereby presentedwith reference to the blight phenomena. Blight refers to symptomsaffecting plants in response to infection by a pathogenic organism. Itis typically associated with a brown color and a dry leaf. According tothe present invention, an automatic search for blight is carried outusing the acquired high resolution multispectral image. The acquiredimage first undergoes color manipulation, as described above, tostrengthen and isolate the blight's appearance from the other objects ofthe image. The usage of various combinations between individual narrowbands from the RGB color space, CMYK color space, the HSL color spaceand possibly other color spaces, enables a substantial separation ofblight infected leaves from their surrounding background. This istypically the green part of the leaf or the brown color of the soil.

Successful color manipulation typically exhibits the blight part veryclearly, usually with two (or more) pronounced dominant colors. Forconvenience, these are termed the principal color and the secondarycolor. These color values depend on the specific color manipulationperformed. FIG. 10 illustrates this example. Here the color manipulationconsisted of taking the image difference between the visual imagecomposed of the RGB channels and the magenta channel associated with theCMYK color of the image. The leaf at the center clearly shows twocolors—brown and purple. The automatic blight detection algorithm firstextracts all pixels associated with the principal color and applies ablob partition on this set. Blobs possessing pixels associated with thesecondary color in their immediate vicinity are declared as associatedwith blight.

While this principle of color manipulation has been exemplified abovewith reference to blight, it will be appreciated that it can be utilizedin many different applications, whether searching for different types ofpests or diseases or monitoring totally different objects. The importantsteps are capturing multi-spectral images and, after blob partitioning,performing division of the images to various color spaces and crossingselected color channels to cause the sought objects to stand out fromthe remainder of the image.

It will be appreciated that, while the present invention has beenexemplified above with regard to agricultural surveys, it is alsoapplicable to many other situations wherein it is necessary to scan anarea on land or in the sea in order to detect local irregularities forfurther investigation, for example, searching for schools of fish orland mines or other surface irregularities on the ground.

While the invention has been described with respect to a limited numberof embodiments, it will be appreciated that many variations,modifications and other applications of the invention may be made. Itwill further be appreciated that the invention is not limited to whathas been described hereinabove merely by way of example. Rather, theinvention is limited solely by the claims which follow.

REFERENCES

J. Hoshen and R. Kopelman (1976). “Percolation and ClusterDistribution”. Phys. Rev. B. 1(14): 3438-3445

http://jpe.oxfordjournals.org/content/1/1/9.full

Remote Sensing Imagery in Vegetation Mapping: a Review

Reference to the US Department of Agriculture regarding remote sensingand spectral bands.

www.mdpi.com%2F2072-4292%2F6%2F6%2F5257%2Fpdf&usg=AFQjCNErQYE2iPKfPKwtJYLnXLB0jWN5KA&sig2=9bMbgRXFMNgmqMIrr2TWqA An Airborne Multi-spectral ImagingSystem Based on Two Consumer-Grade Cameras for Agricultural RemoteSensing

“Systematic biology (hereafter called simply systematics) (a) providesscientific names for organisms, (b) describes them, (c) preservescollections of them, (d) provides classifications for the organisms,keys for their identification, and data on their distributions, (e)investigates their evolutionary histories, and (f) considers theirenvironmental adaptations.” ***

*** Wikipedia—Systematics

UAV imagery, as described by the US DOA uses at best only a pixel per0.1 m (10 cm), which is ×12.5 lower resolution than the scenariodescribed above, while a use of a 50 mm lens in the aforementionedscenario, will result in three meters altitude in ×50 resolutioncompared to the best resolution of the DOA research—over 100 pixels persquare centimeter!

Sensing Fields in this Kind of Resolution Allows the Identification ofthe Pest Type, and Even Remote Differentiation Between Aphids and Flies.

Chambolle, A. (2004). “An algorithm for total variation minimization andapplications”. Journal of Mathematical Imaging and Vision. 20: 89-97.

1. A method for multi-spectral imagery acquisition and analysis, themethod comprising: capturing multi-spectral aerial images according topre-defined survey parameters at a pre-selected resolution;automatically performing preliminary analysis using large scale blobpartitioning of the captured images in real or near real time; detectingirregularities in the analyzed image data according to the pre-definedsurvey parameters and providing an output corresponding thereto;associating GPS data with the detected irregularities; and determining,from said output, whether to perform a second stage of image acquisitionat a higher resolution than said pre-selected resolution and analysisusing small scale blob partitioning.
 2. The method according to claim 1,further comprising: directing an image acquisition device, to captureadditional multi-spectral images of at least one of said detectedirregularities at higher resolution than said pre-selected resolutionusing said associated GPS data; and performing analysis using smallscale blob partitioning of the additional multi-spectral images in realor near real time.
 3. The method according to claim 1, wherein said stepof determining is performed automatically.
 4. The method according toclaim 2, wherein the step of directing includes automatically directingthe image acquisition device.
 5. The method according to claim 1,wherein the step of determining includes: constructing a binary map ofthe image, where pixels associated with predefined range of parametervalues is set to 1 and the rest of the pixels is set to 0; constructinga set of all possible blobs associated with the binary map by rasterscanning the binary map, finds blobs along the scanning process andassigning them with a running index; defining a group of blobs as anirregularity when: a. a number of pixels belonging to a given singleblob exceeds a predefined threshold parameter; or b. a number of pixelsbelonging to a set of blobs, all confined to a given radius (from agravity center of the set of blobs), exceeds a predefined threshold. 6.The method according to claim 2, further comprising: comparingmorphology of at least one of said detected irregularities withmorphology of known objects.
 7. The method according to claim 1, whereinsaid multi-spectral images include at least two images selected from thegroup including visible and NIR images.
 8. A system for multi-spectralimagery acquisition and analysis, the system comprising: at least onemulti-spectral image capturing device; a processor coupled to the imagecapturing device; the processor running an image processing moduleincluding a blob partitioning module to automatically analyze capturedimages by blob partitioning according to predefined survey parametersand provide output corresponding to irregularities on each image fallingwithin said predefined survey parameters; wherein the blob partitioningmodule is operative to implement both large scale blob partitioning andsmall scale blob partitioning and a geographical location indicatoradapted and configured to provide an indication of a geographicallocation of the irregularities; the processor being configured toautomatically determine whether to direct one of the at least onemulti-spectral image capturing devices to said indicated geographicallocation to capture images of said irregularities in response to saidoutput.
 9. The system according to claim 8, wherein the processor is intwo way communication with a user for exchanging data and instructions.10. A method for identifying objects in multi-spectral images, themethod comprising: analyzing high resolution multi-spectral imagesaccording to pre-defined object parameters; when parameters within thepre-defined object parameters are found, performing blob partitioning onsaid images containing such parameters to identify blobs; comparingobjects confined to said blobs to pre-defined reference parameters toidentify objects having said object parameters.
 11. The method accordingto claim 10, further comprising classifying said objects before the stepof comparing.
 12. The method according to claim 10, further comprising:partitioning the acquired images into a confined size sub-images setaccording to the survey type, matching an image size to that of theblobs; and identifying the objects within the set of sub-images byeither one of: sending the set of these sub-images to a remote serverfor identification; or identifying the object on the spot by activatinga local identification code.
 13. The method according to claim 10,further comprising activating an internal pattern matching programbetween the blobs and reference patterns in order to detect a selectedSimple Morphology Structure (SMS) object in the image.
 14. The methodaccording to claim 10, further comprising: converting the image to agray level image that undergoes a gray level blob-based partitioning; abinary map of each of the gray level values is subject to aHoshen-Kopelman cluster labeling algorithm and a set of all possibleblobs consisting of all gray level values is created and stored;extracting, from this set of calculated blobs, only small blobs having asize of smaller than a predefined threshold number of pixels;partitioning the image to several, equal size sub-images; counting anumber of small blobs in each sub-image; and declaring detection of thepresence of Simple Morphology Structure pests when a number of smallblobs in a given percentage of the sub-images exceeds a pre-definedcalibrated threshold value.
 15. The method according to claim 10,further comprising: converting the image to a gray level image thatundergoes a gray level blob partition; selecting, from the set ofpartitioned blobs, blobs having predefined very small size andregistering their gravity centers; extracting sub images around eachregistered gravity center; and comparing the extracted sub images toreference images of Simple Morphology Structure pests, using a machinevision method; and declaring detection of Simple Morphology Structurepests when the correlation exceeds a preset threshold value, and/orsuccessful classification is flagged.
 16. The method according to claim10, further comprising: performing blob partitioning on themulti-spectral images; selecting small blobs having a color that standswithin a close distance to a pre-selected reference color that typicallycharacterizes the Simple Morphology Structure pests, where the distancebetween two given RGB colors is defined as follows:If X1={R1,G1,B1} and X2={R2,G2,B2}ThenDistance[X1,X2]=Max{Abs[R1−R2], Abs[G1−G2], Abs[B1−B2]}.
 17. The methodaccording to claim 10, further comprising: partitioning an acquiredgray-level image X of a Visible image into several very similarnon-overlapping images, by forming a row wise and column wiseinterlacing operator, resulting in forming four sub images X1, X2, X3and X4 whose union accurately spans the original image; defining image Aas an acceleration image of X: A=(X1+X4−2*X3); will result in an image Athat enhances contrasts; extracting from A a set of a pre-selectednumber of pixels having the highest brightness value to yield a set ofpixels; extracting small sub images around the set of calculated highvalue pixels; comparing their content to reference Simple MorphologyStructure pests' images residing in a memory; and flagging presence ofSimple Morphology Structure pests when there is a high correlation. 18.The method according to claim 10, further comprising: converting aVisible or NIR image to a gray scale image; causing the gray level ofthe images to undergo an edge detection operator; from the resultingedge detected image, a set of pixels whose intensity values exceed agiven threshold value is selected; extracting small sub images aroundthese pixels; comparing content of said sub images against knownreference images using a correlation method; and flagging pest detectionif the correlation measure exceeds a pre designed threshold value. 19.The method according to claim 10, further comprising: performing abinary threshold operation on a gray level image of a Visible or NIRimage; from the resulting image, selecting a set of pixels whoseintensity values exceed a predefined threshold value; extracting smallsub images around these pixels; comparing content of the extracted smallsub images against known reference images using a correlation method;and flagging Simple Morphology Structure pest detection when acorrelation measure exceeds a pre designed threshold value.
 20. Themethod according to claim 10, further comprising: performing gray levelconversion and histogram equalization on an acquired image; performingblob partitioning on the gray level image; selecting blobs having amoderate to high number of pixels and extracting sub images surroundingthe gravity centers of these blobs having a radius less than apreselected size; performing further blob partition on each of theselected sub images; arranging the calculated blobs with respect totheir size in pixels; extracting a selected number of large blobs andcreating a binary image comprising their associated pixels; in case aComplex Morphology Structure pest is “captured” in the extracted subimage, two morphology binary operators are applied on the binary mapcomprising the large blobs: a binary dilate operator and a binary erodeoperator; calculating a difference between the dilated and the erodedimages, resulting in an image that approximately captures boundaries ofa pest morphology; and comparing the calculated boundaries with storedreference images of CMS pest boundaries
 21. The method according toclaim 10, further comprising: converting a Visible image or a NIR imageto a gray level image; performing blob partition on the gray levelimage; extracting blobs having a selected pixels size; extracting animage surrounding each such blob; and classifying each such image in aremote server to a category of CMS pests using a machine vision method.22. The method according to claim 10, further comprising: extractingimages of blobs having a relatively large number of pixels; calculatingboundaries of likely objects residing in the extracted images; applyingan Image Corner Filter On each such image, counting a number ofcalculated corner coordinates; and if the calculated number of cornercoordinates exceeds a given threshold a detection of Citrus Leafminer isdeclared by indirect identification.
 23. The method according to claim10, further comprising: performing color manipulation on multi-spectralimages of a plant to strengthen colors in the plant that are associatedwith blight; performing blob partitions to a set of all pixels in theimage associated with a principal dominant color, using a pre-definedtolerance; seeking a match for the detected principal color blobs withpixels associated with a secondary dominant color in the immediatevicinity of that blob; declaring a blight leaf when such a match isfound.
 24. The method according to claim 10, further comprising:performing color manipulation on a high resolution multi-spectral imageto strengthen and isolate an object's appearance from other objects inthe image; performing blob partitioning on the manipulated image;dividing the images to color channels according to at least two colorspaces; and combining selected color channels from the at least twocolor spaces to enhance the object's appearance.
 25. The methodaccording to claim 24, further comprising performing cross detection ofvarious color combinations to improve object separation from thebackground color.
 26. The method according to claim 24, wherein the stepof combining further includes incorporating the 650 nm red band of themulti-spectral spectrum.